Importing the Libraries

In [1]:
import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
import plotly.graph_objects as go
import plotly.express as px
In [2]:
today = date.today()
d1 = today.strftime("%Y-%m-%d")
end_date= d1
d2 = date.today() - timedelta(days=365)
d2 = d2.strftime("%Y-%m-%d")
start_date = d2
data =  yf.download ('GOOG', start=start_date, end=end_date, progress=False)
data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())

        Date        Open        High         Low       Close   Adj Close  \
0 2022-06-27  118.934998  119.250000  116.000748  116.622498  116.622498   
1 2022-06-28  116.350998  117.856499  112.444000  112.571503  112.571503   
2 2022-06-29  112.148499  113.664497  111.554001  112.256500  112.256500   
3 2022-06-30  110.499496  111.329803  107.309998  109.372498  109.372498   
4 2022-07-01  108.336998  109.806351  107.105003  109.081001  109.081001   

     Volume  
0  32840000  
1  28232000  
2  18628000  
3  38046000  
4  31028000
In [3]:
figure = go.Figure(data=[go.Candlestick(x=data["Date"], open = data["Open"],
                                        high=data["High"], low=data["Low"],
                                        close=data["Close"])])

figure.update_layout(title ="Google Stock Price Analysis", xaxis_rangeslider_visible=False)

figure.show()
In [4]:
figure = px.bar (data , x="Date",y="Close")
figure.show()
In [5]:
figure = px.line(data, x='Date', y='Close', title='Stock Market Analysis with Rangeslider')
figure.update_xaxes (rangeslider_visible=True)
figure.show()
In [6]:
figure = px. line(data, x='Date', y='Close',title='Stock Market Analysis with Time Period Selectors')

figure.update_xaxes( rangeselector=dict(buttons=list([dict(count=1, label="1m",
                                step="month", stepmode="backward"), dict(count=6, 
                                label="6m", step="month", stepmode="backward"), 
                               dict(count=3, label="3m", step="month", stepmode="backward"), 
                              dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])))

figure.show()
In [7]:
figure = px.scatter (data, x='Date', y='Close', range_x=['2022-06-20', '2023-06-19'],
                     title= "Stock Market Analysis by Hiding Weekend Gaps")

figure.update_xaxes( rangebreaks=[ dict(bounds=["sat", "sun"])])

figure.show()